Mostly, machine learning (ML) based automation systems are supervised systems, and primarily rely on labeled examples coded by analysts for learning specific tasks, such as classification. The idea to use ML-based automation systems has led to significant contributions to domain adaptation and transfer learning (DA/TL) techniques. The DA/TL techniques leverage knowledge from one or multiple previous (source) domains to learn the task in the new (target) domain.
Advancements in DA/TL techniques are also exploited in same-domain and cross-domain text classification. However, in certain scenarios, the implementation of the DA/TL techniques in cross-domain classification may be cumbersome due to dissimilar data distributions and disparate label sets associated with different source domains. Thus, an advanced technique may be desired that may efficiently perform cross-domain classification irrespective of the dissimilarity in data distributions and disparity in label sets associated with different source domains.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.